tf.nn.atrous_conv2d 实例

import numpy as npimport tensorflow as tfx_image = tf.placeholder(tf.float32,shape=[5,5])x = tf.reshape(x_image,[1,5,5,1])#Filter: WW_cpu = np.array([[3,1,1],[0,-2,0],[0,-1,1]],dtype=np.float32)W = tf.Variable(W_cpu)W = tf.reshape(W, [3,3,1,1])strides=[1, 1, 1, 1]#没用到padding='VALID'y = tf.nn.atrous_conv2d(x, W, 2, padding)x_data = np.array([[1,0,0,0,0],[2,1,1,2,1],[1,1,2,2,0],[2,2,1,0,0],[2,1,2,1,1]],dtype=np.float32)with tf.Session() as sess:    init = tf.initialize_all_variables()    sess.run(init)    x = (sess.run(x, feed_dict={x_image: x_data}))    W = (sess.run(W, feed_dict={x_image: x_data}))    y = (sess.run(y, feed_dict={x_image: x_data}))    print "The shape of x:\t", x.shape, ",\t and the x.reshape(5,5) is :"    print x.reshape(5,5)    print ""    print "The shape of x:\t", W.shape, ",\t and the W.reshape(3,3) is :"    print W.reshape(3,3)    print ""    print "The shape of y:\t", y.shape, ",\t and the y.reshape(1) is :"    print y.reshape(1)    print ""